Shristi Mangal Pal 
John Hopfield and Geoffrey Hinton have been awarded the Nobel Prize for Physics for their research into enabling machine learning with artificial neural networks.
It follows yesterday’s awarding of the Nobel Prize in Physiology to Victor Ambros and Gary Ruvkun. The award for chemistry will be announced on Wednesday (October 9).
The Nobel Prize in Physics has been awarded 118 times to 227 recipients from 1901 to 2024. John Bardeen is the only person to win it twice (in 1956 and 1972), so 226 individuals have received the prize.
In announcing the award, the committee led by Hans Ellegren noted that machine learning “has long been important for research, including the sorting and analysis of vast amounts of data.”
Hinton: ‘Machine learning will exceed people in intellectual abilities’
British-Canadian Nobel laureate, computer scientist and cognitive psychologist Geoffrey Hinton spoke to the press shortly after the winners were announced.
“I am flabbergasted, I had no idea this would happen, I am very surprised,” Hinton said, when asked how he felt about being a Nobel laureate. He assures that advancements in neural networks will have a huge influence.
“This will be comparable with the industrial revolution. Machine learning will exceed people in intellectual abilities,” he added.
While he listed the numerous applications, such as in healthcare, AI assistants and increase in work productivity, he also pointed out that Machine Learning poses a threat that things could get out of control.
Hinton admitted to using ChatGPT 4 a lot. “I don’t totally trust it, as sometimes it can hallucinate,” he added.
“I am in a cheap hotel in California without very good internet or phone connection,” he said, as to where he was when he received the news.
AI, machine learning and deep learning – simply explained
Terms like machine learning, AI and deep learning were used heavily at the Nobel Prize announcements. Advancements in computer science have led to extensive research in these fields, Ellegren, the secretary general of the Royal Swedish Academy of Sciences, said.
The tech company IBM describes AI as the umbrella term for machines that mimic human intelligence.
Meanwhile, machine learning is a subset of AI. It focuses on improving AI systems by teaching them to learn from data and make better predictions.
Deep learning, which is the focus of Hopfield’s and Hinton’s research, is a more powerful version of machine learning. Deep learning uses deeper layers of neural networks.
Neural networks are the building blocks of deep learning models, just like neurons are the building blocks in the human nervous system.
Neural networks form the core of deep learning. These are made up of layers of nodes like neurons in the brain. A simple neural network has only a few layers, but a deep learning model must have more than three layers, which gives it the power to solve more complex problems.
‘Godfather of AI’: Who is Geoffrey Hinton?
Hailed as the ‘Godfather of AI’ and a pioneer in that field, Geoffrey Hinton has expressed regret about his role in advancing AI, particularly regarding its potential future impacts.
“If I hadn’t done it, somebody else would have,” he told the New York Times last year.
In 2017, the 76-year-old co-founded the Vector Institute in Toronto and became its chief scientific adviser.
A year later, in 2018, Hinton, along with Yoshua Bengio and Yann LeCun, received the prestigious Turing Award, often called the “Nobel Prize of Computing”, for their ground-breaking work in deep learning.
The trio, dubbed the “Godfathers of Deep Learning”, continued to give public talks together.
In May 2023, Hinton publicly resigned from his position at Google, where he had worked for over a decade. He stepped down to freely express his concerns about the risks associated with AI, including its potential misuse, job displacement and existential threats from advanced AI systems.
He emphasised the need for collaboration among AI developers to establish safety guidelines and prevent harmful outcomes.
This article was originally published on DW.